Creative quantum error correction, DNA analysis, 3D lattice visualization, and AI-powered IRC orchestration.
This project builds on the E8 Triality Framework — uniting symmetry, information, and computation — under the guiding relation:
Φ = π / 2 SCL DIAG + [ 1 , −2 , 1 ]
This defines a ternary balance across the quantum–classical boundary,
where each component of the [ 1, −2, 1 ] vector encodes a reversible polarity
between signal, coherence, and loss.
- Fusion-QEC (Photonic) — modular, loss-tolerant error correction inspired by MBQC fusion gates.
- Information Entropy → Signal Mapping — translating quantum noise dynamics into musical structure.
- Triality Framework — treating computation, geometry, and perception as three projections of a single invariant form.
An integrated assistant linking simulation, music, and conversation:
| Feature | Description |
|---|---|
| QuTiP-based Steane Code | [[7, 1, 3]] simulation with depolarizing noise and pseudo-threshold (ηₜₕᵣ ≈ 9.3 × 10⁻⁵) |
| MIDI Export | Converts error metrics → tempo, eigenvalues → velocity, logical errors → E-minor arpeggios |
| LLM Integration | Conversational AI for code generation, simulation commentary, and live moderation |
| IRC Protocol | Real-time Q&A, simulation control, and generative music triggers |
Experience the sound of symmetry meeting error correction.
This audio was generated directly from QEC simulation data using the
Φ = π / 2 SCL DIAG + [ 1 , − 2 , 1 ] mapping.
Each amplitude and interval reflects the balance between signal, coherence, and loss —
a musical rendering of the Fusion-QEC Triality Model.
- 🖼 Figure 1 — View / Download
- 🖼 Figure 2 — View / Download
- 🎵 Sonification MP3 — Listen: QEC Fault Lines Sonification
- 📄 Benchmark Report (PDF) — Read: QEC Benchmark Report
- 📦 Full Repo Archive (ZIP) — Download: QEC_Repo.zip
- 💻 Sonification Script — View Source: sonify_triality.py
All resources are located in the repository root for direct access and reproducibility.
- Language: Python 3.11+
- Core Dependencies: NumPy, Pandas, QuTiP, Plotly, Mido
- Environment: Arch Linux / PipeWire audio
- Output: Live audio or export to WAV (
e8_triality.wav) - Data Input:
qec_output.csvorqec_data_prepared.csv
# Install dependencies
pip install -r requirements.txt
# Generate sonification (auto-fallback to WAV)
python sonify_triality.py
For more complex experiments, see QEC_Benchmark_Report.pdf
or remix the exported audio in your preferred DAW.
© 2025 QSOLKCB / Trent Slade. All rights reserved.
Open collaboration welcome under MIT License.